Intraamniotic infection is a major risk factor for preterm birth (the greatest single agent of perinatal morbidity and mortality) as well as a contributor to the development of cerebral palsy and other significant childhood diseases. As such, its diagnosis needs to be both reliable (reproducible in the same patient, and across patients and institutions) and valid (consistently predictive of important clinical features of infection, including severity, duration and risk of sequelae such as neonatal sepsis). Unfortunately, current diagnostic pathology "gold standards" are neither reliable nor have they been validated against measures other than "group consensus", a poor substitute for biologically valid endpoints such as amniotic fluid or cord blood proteomics. Only a handful of US pathologists possess expertise in this diagnosis, with most of these physicians located in academic medical centers, limiting both the absolute numbers of infants who can be provided services as well as where such care can be provided. The vast majority of US maternity hospitals therefore effectively lack access to the expert personnel needed to provide such care, resulting in a system in which only a small proportion of newborns can be accurately and reliably assessed for exposure to intraamniotic infections. In this Phase 1 proposal, we will, first, make our algorithms robust to the variability in hematoxylin and eosin staining that would be expected in slides prepared from diverse hospital laboratories. Next, in order to validate these measures, we will take advantage of 2 large data sets in which placentas have already been collected, sampled, sectioned and stained, and slides have been scored (using current standard methods) and digitized, and a team of expert pathologists who have been involved with the data sets and are committed to the project. Using these resources, we will determine the reliability of our product, image analysis software that is a toolbox of algorithm-based image segmentation tools that can replace the current "best practice" (semi-quantitation of neutrophil infiltrates) in routine hematoxylin and eosin (H&E) stained slides. Validation against both expert pathologists and biologically germane endpoints (amniotic fluid or cord blood proteomics related to infection/inflammation associated molecules) will prepare this diagnostic tool for market introduction to provide state of the art diagnostic care for infants beyond the reach of the small cadre of "expert" placental pathologists. In effect, this tool will open the potential for reliable, reproducible and valid diagnoses to be performed at any hospital in the world that can produce a hematoxylin and eosin stained slide. PUBLIC HEALTH RELEVANCE: The fetal inflammatory response, defined as elevated levels of inflammatory cytokines in cord blood and by vasculitis in the umbilical and chorionic vessels of the placenta, predicts recurrence risk for preterm birth (optimizing next pregnancy outcomes), and risks of intraventricular hemorrhage, cerebral white matter damage, cerebral palsy, childhood asthma and lung damage more generally. The current histologic tools used to measure inflammation are limited to a semiquantative estimation of neutrophil number with limited reproducibility even among "experts". Placental Analytics has developed a set of image processing algorithms for digitized histology slides that promises reliable quantification of the current "gold standard" for histologic assessment of intraamniotic infection (i.e., neutrophil number), as well as quantification of neutrophil karyorrhexis that may correlate with age/duration of infection more precisely than any current histologic method.